Investigation of spatial distribution of oil and gas resource and accurate prediction of the geographic location of its undiscovered resource is significant for reducing exploration risk and improving exploration bene...Investigation of spatial distribution of oil and gas resource and accurate prediction of the geographic location of its undiscovered resource is significant for reducing exploration risk and improving exploration benefit.A new method for predicting spatial distribution of oil resource is discussed in this paper.It consists of prediction of risk probability in petroleum exploration and simulation of hydrocarbon abundance. Exploration risk probability is predicted by multivariate statistics,fuzzy mathematics and information processing techniques.A spatial attribute database for sample wells was set up and the Mahalanobis distance and Fuzzy value of given samples were obtained.Then,the Bayesian formula was used to calculate the hydrocarbon-bearing probability at the area of exploration wells.Finally,a hydrocarbon probability template is formed and used to forecast the probability of the unknown area. The hydrocarbon abundance is simulated based on Fourier integrals,frequency spectrum synthesis and fractal theory.Firstly,the fast Fourier transformation（FFT） is used to transform the known hydrocarbon abundance from the spatial domain to the frequency domain,then,frequency spectrum synthesis is used to produce the fractal frequency spectrum,and FFT is applied to get the phase information of hydrocarbon-bearing probability.Finally,the frequency spectrum simulation is used to calculate the renewed hydrocarbon abundance in the play. This method is used to predict the abundance and possible locations of the undiscovered petroleum accumulations in the Nanpu Sag of the Bohai Bay Basin,China.The prediction results for the well-explored onshore area of the northern Nanpu Sag agree well with the actual situations.For the less-explored offshore areas in the southern Nanpu Sag,the prediction results suggest high hydrocarbon abundance in Nanpu-1 and Nanpu-2,providing a useful guiding for future exploration.展开更多
With the growing penetration of wind power in power systems, more accurate prediction of wind speed and wind power is required for real-time scheduling and operation. In this paper, a novel forecast model for shortter...With the growing penetration of wind power in power systems, more accurate prediction of wind speed and wind power is required for real-time scheduling and operation. In this paper, a novel forecast model for shortterm prediction of wind speed and wind power is proposed,which is based on singular spectrum analysis(SSA) and locality-sensitive hashing(LSH). To deal with the impact of high volatility of the original time series, SSA is applied to decompose it into two components: the mean trend,which represents the mean tendency of the original time series, and the fluctuation component, which reveals the stochastic characteristics. Both components are reconstructed in a phase space to obtain mean trend segments and fluctuation component segments. After that, LSH is utilized to select similar segments of the mean trend segments, which are then employed in local forecasting, so that the accuracy and efficiency of prediction can be enhanced. Finally, support vector regression is adopted forprediction, where the training input is the synthesis of the similar mean trend segments and the corresponding fluctuation component segments. Simulation studies are conducted on wind speed and wind power time series from four databases, and the final results demonstrate that the proposed model is more accurate and stable in comparison with other models.展开更多
文摘Investigation of spatial distribution of oil and gas resource and accurate prediction of the geographic location of its undiscovered resource is significant for reducing exploration risk and improving exploration benefit.A new method for predicting spatial distribution of oil resource is discussed in this paper.It consists of prediction of risk probability in petroleum exploration and simulation of hydrocarbon abundance. Exploration risk probability is predicted by multivariate statistics,fuzzy mathematics and information processing techniques.A spatial attribute database for sample wells was set up and the Mahalanobis distance and Fuzzy value of given samples were obtained.Then,the Bayesian formula was used to calculate the hydrocarbon-bearing probability at the area of exploration wells.Finally,a hydrocarbon probability template is formed and used to forecast the probability of the unknown area. The hydrocarbon abundance is simulated based on Fourier integrals,frequency spectrum synthesis and fractal theory.Firstly,the fast Fourier transformation（FFT） is used to transform the known hydrocarbon abundance from the spatial domain to the frequency domain,then,frequency spectrum synthesis is used to produce the fractal frequency spectrum,and FFT is applied to get the phase information of hydrocarbon-bearing probability.Finally,the frequency spectrum simulation is used to calculate the renewed hydrocarbon abundance in the play. This method is used to predict the abundance and possible locations of the undiscovered petroleum accumulations in the Nanpu Sag of the Bohai Bay Basin,China.The prediction results for the well-explored onshore area of the northern Nanpu Sag agree well with the actual situations.For the less-explored offshore areas in the southern Nanpu Sag,the prediction results suggest high hydrocarbon abundance in Nanpu-1 and Nanpu-2,providing a useful guiding for future exploration.
基金supported by the Guangdong Innovative Research Team Program(No.201001N0104744201)the State Key Program of the National Natural Science Foundation of China(No.51437006)
文摘With the growing penetration of wind power in power systems, more accurate prediction of wind speed and wind power is required for real-time scheduling and operation. In this paper, a novel forecast model for shortterm prediction of wind speed and wind power is proposed,which is based on singular spectrum analysis(SSA) and locality-sensitive hashing(LSH). To deal with the impact of high volatility of the original time series, SSA is applied to decompose it into two components: the mean trend,which represents the mean tendency of the original time series, and the fluctuation component, which reveals the stochastic characteristics. Both components are reconstructed in a phase space to obtain mean trend segments and fluctuation component segments. After that, LSH is utilized to select similar segments of the mean trend segments, which are then employed in local forecasting, so that the accuracy and efficiency of prediction can be enhanced. Finally, support vector regression is adopted forprediction, where the training input is the synthesis of the similar mean trend segments and the corresponding fluctuation component segments. Simulation studies are conducted on wind speed and wind power time series from four databases, and the final results demonstrate that the proposed model is more accurate and stable in comparison with other models.
